| [1] |
SABRI M H M, AHMAD M R, TAKAYANAGI Y, et al. Observation of tropical positive cloud-to-ground flashes accompanied by chaotic and regular pulse trains[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2024, 261: 106285. doi: 10.1016/j.jastp.2024.106285.
|
| [2] |
FENG Jizhou, YUAN Shanfeng, JIANG Rubin, et al. The impact of intracloud negative branches on continuing current in negative cloud-to-ground lightning[J]. Geophysical Research Letters, 2025, 52(17): e2025GL116612. doi: 10.1029/2025GL116612.
|
| [3] |
WU Ting, WANG Daohong, and TAKAGI N. High-accuracy classification of radiation waveforms of lightning return strokes[J]. Journal of Geophysical Research: Atmospheres, 2023, 128(14): e2023JD038715. doi: 10.1029/2023jd038715.
|
| [4] |
XIAO Lilang, CHEN Weijiang, WANG Yu, et al. Toward an interpretable CNN model for the classification of lightning-produced VLF/LF signals[J]. Journal of Geophysical Research: Atmospheres, 2023, 128(22): e2023JD039517. doi: 10.1029/2023JD039517.
|
| [5] |
KITAGAWA N and BROOK M. A comparison of intracloud and cloud-to-ground lightning discharges[J]. Journal of Geophysical Research: Atmospheres, 1960, 65(4): 1189–1201. doi: 10.1029/JZ065i004p01189.
|
| [6] |
SHAO Xuanmin, STANLEY M, REGAN A, et al. Total lightning observations with the new and improved Los Alamos Sferic Array (LASA)[J]. Journal of Atmospheric and Oceanic Technology, 2006, 23(10): 1273–1288. doi: 10.1175/JTECH1908.1.
|
| [7] |
BETZ H D, SCHMIDT K, OETTINGER P, et al. Lightning detection with 3-D discrimination of intracloud and cloud-to-ground discharges[J]. Geophysical Research Letters, 2004, 31(11): L11108. doi: 10.1029/2004GL019821.
|
| [8] |
张旭荣, 张妙兰, 刘新中. 小波变换在核爆电磁脉冲信号识别中的应用[J]. 电子与信息学报, 1999, 21(5): 710–712.
ZHANG Xurong, ZHANG Miaolan, and LIU Xinzhong. Studies of recognition methods of nuclear and lightning impulse signals with applications of wavelet transform[J]. Journal of Electronics & Information Technology, 1999, 21(5): 710–712.
|
| [9] |
HARIS F A, KADIR M Z A, SUDIN S, et al. Automated negative lightning return strokes classification system[J]. Journal of Physics: Conference Series, 2021, 2107: 012022. doi: 10.1088/1742-6596/2107/1/012022.
|
| [10] |
ZHU Shunxing, ZHANG Yang, FAN Yanfeng, et al. A lightning classification method based on convolutional encoding features[J]. Remote Sensing, 2024, 16(6): 965. doi: 10.3390/rs16060965.
|
| [11] |
ZHU Yanan, BITZER P, RAKOV V, et al. A machine-learning approach to classify cloud-to-ground and intracloud lightning[J]. Geophysical Research Letters, 2021, 48(1): e2020GL091148. doi: 10.1029/2020GL091148.
|
| [12] |
MOHAMMED A and KORA R. A comprehensive review on ensemble deep learning: Opportunities and challenges[J]. Journal of King Saud University-Computer and Information Sciences, 2023, 35(2): 757–774. doi: 10.1016/j.jksuci.2023.01.014.
|
| [13] |
GUI Jie, CHEN Tuo, ZHANG Jing, et al. A survey on self-supervised learning: Algorithms, applications, and future trends[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 9052–9071. doi: 10.1109/tpami.2024.3415112.
|
| [14] |
LU Jingyu. The dataset and MAE model code for “an efficient lightning classifier using a self-supervised learning neural network”[EB/OL]. Zenodo. https://doi.org/10.5281/zenodo.14556712, 2024.
|
| [15] |
PU Yunjiao, CUMMER S A, LYU Fanchao, et al. Unsupervised clustering and supervised machine learning for lightning classification: Application to identifying EIPs for ground-based TGF detection[J]. Journal of Geophysical Research: Atmospheres, 2023, 128(9): e2022JD038369. doi: 10.1029/2022jd038369.
|
| [16] |
CHENG Mingyue, TAO Xiaoyu, LIU Zhiding, et al. TimeMAE: Self-supervised representations of time series with decoupled masked autoencoders[C]. Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining, Boise, USA, 2026: 498–508.
|
| [17] |
PENG Changzhi, LIU Feifan, ZHU Baoyou, et al. A convolutional neural network for classification of lightning LF/VLF waveform[C]. Proceedings of the 2019 11th Asia-Pacific International Conference on Lightning, Hong Kong, China, 2019: 1–4. doi: 10.1109/APL.2019.8815977.
|
| [18] |
WANG Jiaquan, HUANG Qijun, MA Qiming, et al. Classification of VLF/LF lightning signals using sensors and deep learning methods[J]. Sensors, 2020, 20(4): 1030. doi: 10.3390/s20041030.
|
| [19] |
GAO Chao, WANG Jiaquan, ZHOU Xiao, et al. Classification of lightning electric field waveform based on deep residual one-dimensional convolutional network[M]. MENG Hongying, LEI Tao, LI Maozhen, et al. Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Cham: Springer, 2021: 1648–1659. doi: 10.1007/978-3-030-70665-4_179.
|
| [20] |
QIAN Zheng, WANG Dongdong, SHI Xiangbo, et al. Lightning identification method based on deep learning[J]. Atmosphere, 2022, 13(12): 2112. doi: 10.3390/atmos13122112.
|
| [21] |
ZHANG Xiaoyi, WANG Caixia, and TIAN Yangmeng. Classification and feature extraction of lightning electric field waveforms based on machine learning[C]. Proceedings of the 2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence, Beijing, China, 2022: 199–204. doi: 10.1109/CCAI55564.2022.9807742.
|
| [22] |
FERREIRA G A V S, LEAL A F R, DIGANGI E A, et al. Residual neural network applied to lightning classification in a modern lightning location network[C]. Proceedings of the 37th International Conference on Lightning Protection, Dresden, Germany, 2024: 291–296.
|
| [23] |
XIAO Fang, MA Qiming, SONG Jiajun, et al. Study on multi-station identification technology of lightning electromagnetic pulses (LEMPs) based on deep learning[J]. Sensors, 2025, 25(23): 7217. doi: 10.3390/S25237217.
|